In communications networks, there may be a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the communications network is deployed.
For example, the exponential increase in cellular data usage due to the market introduction of smartphones is evident, and research suggests the increase will continue its pace. For example, it may be expected that, worldwide, smartphone subscriptions will have more than doubled by 2020, and that 70% of the world's population will have a smartphone. In addition to this, it may be expected that mobile video traffic, which currently may be regarded as bandwidth demanding, will grow by approximately 55% per year from 2014-2020, reaching around 60% of all mobile data traffic by the end of that period. This considerable growth in data traffic entails challenges as well as possibilities for network providers and network operators.
For example, data obtained from network service and usage may be used for useful analysis and prediction of future events related to user behavior. Having this knowledge may facilitate for use of advance applications related to radio resource management (RRM). In short, RRM enables for control of certain parameters, such as transmit power, user allocation and handover criteria. RRM adaption may typically be used for resource optimization and for achieving end-user performance improvements.
Previous work in this area involves the use of clustering techniques. In “Mobile Terminal Session SIR Prediction Method Based on Clustering and Classification Algorithms” by Martín-Sacristán, D. et al in Proceedings of the sixteenth annual international conference on Mobile computing and networking, 2010, an improvement was found when using clustering techniques for data of one dimension compared to generic means, suggesting clustering patterns can be found in this kind of data. Co-clustering can also be used for characterization of user behavior in terms of browsing profiles in a network was made.
In terms of a more statistical approach to analyzing traffic and user behavior, “Users in cells: a data traffic analysis” by Laner, M. et al, in the Proceedings of the Wireless Communications and Networking Conference (WCNC), 2012 proposes an extensive statistical analysis aiming to construct statistical models for data traffic in single cells. It was found that there are differences with regards to throughput between cells in a network. Furthermore, an analysis on subscriber mobility and temporal activity patterns can be performed to find that a small fraction of users create the majority of daily traffic and are mostly sporadic.
Machine learning techniques have been considered as well. In “Learning Probabilistic Models of Cellular Network Traffic with Applications to Resource Management” by Paul, U. et al in In IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), 2014, it was assumed that radio access network node loads can be represented as time sequences of multivariate Gaussian random variables, and modelled as a Gaussian Markov Random Field.
The above proposed mechanisms for utilizing cellular data for improving network properties only have a low utilization of available resources. Hence, there may be network properties that are missed.
Hence, there is still a need for better utilization of cellular data for improving network properties.